Skip to main content
SpringerLink
Log in

Ultrasound Features Improve Diagnostic Performance of Ovarian Cancer Predictors in Distinguishing Benign and Malignant Ovarian Tumors

  • Published:
Current Medical Science Aims and scope Submit manuscript

Summary

To determine whether ultrasound features can improve the diagnostic performance of tumor markers in distinguishing ovarian tumors, we enrolled 719 patients diagnosed as having ovarian tumors at Nanfang Hospital from September 2014 to November 2016. Age, menopausal status, histopathology, the International Federation of Gynecology and Obstetrics (FIGO) stages, tumor biomarker levels, and detailed ultrasound reports of patients were collected. The area under the curve (AUC), sensitivity, and specificity of the bellow-mentioned predictors were analyzed using the receiver operating characteristic curve. Of the 719 patients, 531 had benign lesions, 119 had epithelial ovarian cancers (EOC), 44 had borderline ovarian tumors (BOT), and 25 had non-EOC. AUCs and the sensitivity of cancer antigen 125 (CA125), human epididymis-specific protein 4 (HE4), Risk of Ovarian Malignancy Algorithm (ROMA), Risk of Malignancy Index (RMI1), HE4 model, and Rajavithi-Ovarian Cancer Predictive Score (R-OPS) in the overall population were 0.792, 0.854, 0.856, 0.872, 0.893, 0.852, and 70.2%, 56.9%, 69.1%, 60.6%, 77.1%, 71.3%, respectively. For distinguishing EOC from benign tumors, the AUCs and sensitivity of the above mentioned predictors were 0.888, 0.946, 0.947, 0.949, 0.967, 0.966, and 84.0%, 79.8%, 87.4%, 84.9%, 90.8%, 89.1%, respectively. Their specificity in predicting benign diseases was 72.9%, 94.4%, 87.6%, 95.9%, 86.3%, 90.8%, respectively. Therefore, we consider biomarkers in combination with ultrasound features may improve the diagnostic performance in distinguishing malignant from benign ovarian tumors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer, 2015,136(5):E359–386

    Article  CAS  PubMed  Google Scholar 

  2. Kaijser J, Van Belle V, Van Gorp T, et al. Prognostic value of serum HE4 levels and risk of ovarian malignancy algorithm scores at the time of ovarian cancer diagnosis. Int J Gynecol Cancer, 2014,24(7):1173–1180

    Article  PubMed  Google Scholar 

  3. Duffy MJ, Bonfrer JM, Kulpa J, et al. CA125 in ovarian cancer: European Group on Tumor Markers guidelines for clinical use. Int J Gynecol Cancer, 2005,15(5):679–691

    Article  CAS  PubMed  Google Scholar 

  4. Rosen DG, Wang L, Atkinson JN, et al. Potential markers that complement expression of CA125 in epithelial ovarian cancer. Gynecol Oncol, 2005,99(2):266–277

    Article  CAS  Google Scholar 

  5. Paramasivam S, Tripcony L, Crandon A, et al. Prognostic importance of preoperative CA-125 in International Federation of Gynecology and Obstetrics stage I epithelial ovarian cancer: an Australian multicenter study. J Clin Oncol, 2005,23(25):5938–5942

    Article  CAS  PubMed  Google Scholar 

  6. Moore RG, Brown AK, Miller MC, et al. The use of multiple novel tumor biomarkers for the detection of ovarian carcinoma in patients with a pelvic mass. Gynecol Oncol, 2008,108(2):402–408

    Article  CAS  PubMed  Google Scholar 

  7. Wu L, Dai ZY, Qian YH, et al. Diagnostic value of serum human epididymis protein 4 (HE4) in ovarian carcinoma: a systematic review and meta-analysis. Int J Gynecol Cancer, 2012,22(7):1106–1112

    Article  PubMed  Google Scholar 

  8. Drapkin R, von Horsten HH, Lin Y, et al. Human epididymis protein 4 (HE4) is a secreted glycoprotein that is overexpressed by serous and endometrioid ovarian carcinomas. Cancer Res, 2005,65(6):2162–2169

    Article  CAS  PubMed  Google Scholar 

  9. Moore RG, McMeekin DS, Brown AK, et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol Oncol, 2009,112(1):40–46

    Article  CAS  PubMed  Google Scholar 

  10. Li F, Tie R, Chang K, et al. Does risk for ovarian malignancy algorithm excel human epididymis protein 4 and CA125 in predicting epithelial ovarian cancer: a meta-analysis. BMC Cancer, 2012,12:258

    Article  PubMed  PubMed Central  Google Scholar 

  11. Smorgick N, Maymon R. Assessment of adnexal masses using ultrasound: a practical review. Int J Womens Health, 2014,6:857–863

    Article  PubMed  PubMed Central  Google Scholar 

  12. Jacobs I, Oram D, Fairbanks J, et al. A risk of malignancy index incorporating CA 125, ultrasound and menopausal status for the accurate preoperative diagnosis of ovarian cancer. Br J Obstet Gynaecol, 1990,97(10):922–929

    Article  CAS  PubMed  Google Scholar 

  13. Karlsen MA, Sandhu N, Hogdall C, et al. Evaluation of HE4, CA125, risk of ovarian malignancy algorithm (ROMA) and risk of malignancy index (RMI) as diagnostic tools of epithelial ovarian cancer in patients with a pelvic mass. Gynecol Oncol, 2012,127(2):379–383

    Article  PubMed  Google Scholar 

  14. Moore RG, Jabre-Raughley M, Brown AK, et al. Comparison of a novel multiple marker assay vs the Risk of Malignancy Index for the prediction of epithelial ovarian cancer in patients with a pelvic mass. Am J Obstet Gynecol, 2010,203(3):228.e1–6

    Article  Google Scholar 

  15. Wilailak S, Chan KK, Chen CA, et al. Distinguishing benign from malignant pelvic mass utilizing an algorithm with HE4, menopausal status, and ultrasound findings. J Gynecol Oncol, 2015,26(1):46–53

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yanaranop M, Tiyayon J, Siricharoenthai S, et al. Rajavithi-ovarian cancer predictive score (R-OPS): A new scoring system for predicting ovarian malignancy in women presenting with a pelvic mass. Gynecol Oncol, 2016,141(3):479–484

    Article  PubMed  Google Scholar 

  17. Prat J. FIGO’s staging classification for cancer of the ovary, fallopian tube, and peritoneum: abridged republication. J Gynecol Oncol, 2015,26(2):87–89

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Fluss R, Faraggi D, Reiser B. Estimation of the Youden Index and its associated cutoff point. Biom J, 2005,47(4):458–472

    Article  PubMed  Google Scholar 

  19. Tian Y, Wang C, Cheng L, et al. Determination of reference intervals of serum levels of human epididymis protein 4 (HE4) in Chinese women. J Ovarian Res, 2015,8:72

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Zhang P, Wang C, Cheng L, et al. Comparison of HE4, CA125, and ROMA Diagnostic Accuracy: A Prospective and Multicenter Study for Chinese Women With Epithelial Ovarian Cancer. Medicine (Baltimore), 2015,94(52):e2402

    Article  CAS  Google Scholar 

  21. Molina R, Escudero JM, Auge JM, et al. HE4 a novel tumour marker for ovarian cancer: comparison with CA 125 and ROMA algorithm in patients with gynaecological diseases. Tumour Biol, 2011,32(6):1087–1095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Shah CA, Lowe KA, Paley P, et al. Influence of ovarian cancer risk status on the diagnostic performance of the serum biomarkers mesothelin, HE4, and CA125. Cancer Epidemiol Biomarkers Prev, 2009,18(5):1365–1372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Timmerman D, Ameye L, Fischerova D, et al. Simple ultrasound rules to distinguish between benign and malignant adnexal masses before surgery: prospective validation by IOTA group. BMJ, 2010,341:c6839

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sassone AM, Timor-Tritsch IE, Artner A, et al. Transvaginal sonographic characterization of ovarian disease: evaluation of a new scoring system to predict ovarian malignancy. Obstet Gynecol, 1991,78(1):70–76

    CAS  PubMed  Google Scholar 

  25. Van Gorp T, Veldman J, Van Calster B, et al. Subjective assessment by ultrasound is superior to the risk of malignancy index (RMI) or the risk of ovarian malignancy algorithm (ROMA) in discriminating benign from malignant adnexal masses. Eur J Cancer, 2012,48(11):1649–1656

    Article  PubMed  Google Scholar 

  26. Stiekema A, Lok CA, Kenter GG, et al. A predictive model combining human epididymal protein 4 and radiologic features for the diagnosis of ovarian cancer. Gynecol Oncol, 2014,132(3):573–577

    Article  CAS  PubMed  Google Scholar 

  27. Moszynski R, Szubert S, Szpurek D, et al. Usefulness of the HE4 biomarker as a second-line test in the assessment of suspicious ovarian tumors. Arch Gynecol Obstet, 2013,288(6):1377–1383

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Xu Y, Zhong R, He J, et al. Modification of cut-off values for HE4, CA125 and the ROMA algorithm for early-stage epithelial ovarian cancer detection: Results from 1021 cases in South China. Clin Biochem, 2016,49(1–2):32–40

    Article  PubMed  Google Scholar 

Download references

Author information

Author notes

  1. The authors contributed equally to this work.

Authors and Affiliations

  1. Department of Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510632, China

    Yong-ning Chen

  2. Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China

    Yong-ning Chen, Ya-di Zhang, Chan-yuan Li & Shi-peng Gong

  3. Department of Ultrasonography, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China

    Fei Ma

  4. Department of PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China

    Li Chen

Authors
  1. Yong-ning Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ya-di Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chan-yuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shi-peng Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shi-peng Gong.

Additional information

Conflict of Interest Statement

We declare that we have no conflict of interest.

This work was supported by grants from Guangdong Science and Technology Department of China (No. 2016A020215115), Science and Technology Bureau of Tianhe District, Guangzhou, Guangdong (No. 201604KW010), and Science and Technology Bureau of Huadu District, Guangzhou, Guangdong (No. HD15CXY006).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Yn., Ma, F., Zhang, Yd. et al. Ultrasound Features Improve Diagnostic Performance of Ovarian Cancer Predictors in Distinguishing Benign and Malignant Ovarian Tumors. CURR MED SCI 40, 184–191 (2020). https://doi.org/10.1007/s11596-020-2163-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11596-020-2163-7

Key words

Search

Navigation